The invention relates generally to system health assessment, and more specifically to diagnosis and prognosis of system performance, errant system conditions, and abnormal system behavior in an instrumented system.
Complex systems typically cannot tolerate a long down time and so need to be constantly monitored. For example, a semiconductor fabrication facility cannot afford to be offline for an extended period of time. In addition to the loss of wafer production, it takes considerable time to restart the line. A patient monitoring station must have high reliability in order to be useful. Spacecraft must be constantly monitored in order to detect faults and to detect trends in system operation which may lead to faults, so that proactive corrective action can be taken.
It is also important to avoid false positive indications of a system error. It is both costly and time consuming to bring a system down, replace or repair the supposed error, and bring the system back up only to discover that the incorrect remedy was taken.
As advances in technology permit higher degrees of integration both at the component level and at the system level, systems become increasingly more complex. Consequently, improvements for determining system performance and assessing system health are needed, to adequately detect system faults and operational trends that might lead to system failure.
A method and apparatus for diagnosis and prognosis of faults in accordance with embodiments of the invention is based on sensor data and discrete data. In an embodiment of the invention, anomaly detection is made based on a statistical analysis of time-correlated sensor data and on mode of operation of the system as determined by the discrete data. In another embodiment of the invention, anomaly detection is further based on a statistical analysis of individual sensor data.